Detecting and classifying spines (3D)

• Spine-detection settings:
  • Outer range: Maximum distance between the dendritic surface and the potential top of the spine head.
  • Minimum height: Minimum distance between the dendritic surface and the furthest voxel identified in the spine.
  •  Detector sensitivity: Use when detecting individual spines.
  • Minimum count: Adjust to ignore objects that are too small to be considered spines, e.g., background fluorescence/staining. Works in conjunction with Minimum height.
• Keep existing spines: To detect spines in addition to spines previously detected, check the box.
• Filter image noise: If your image contains nonspecific staining and/or fluorescence, such as shot noise, check the box to minimize its effects on the analysis.
• Buttons:
  • Detect All: Automatically detects spines based on the settings defined in the Detection portion of the window.
  • Reset: Resets spine-detection settings to their default values.
  • Clear Last: Deletes the spines detected by the previous round of spine detection.
• Click image to detect all on nearest branch: Check the box and click the desired branch to detect spines on a single branch. This may be useful for quickly identifying appropriate spine detection settings, especially in images that include large complex cells with many processes. Once you're satisfied with the detection settings, clear the checkbox and click Detect All
• Options:
  • Center after each manual detection checkbox: Check the box to automatically center the window at the last point clicked and avoid the need to pan manually while tracing.
  • Show axial smear correction checkbox: Check the box to display the corrected thickness of the tree. Axial smear is automatically calculated after each detection. Or you can click Refresh to update and display the axial smear correction at any time.
  • Allow where varicosity present: Allows spines to be detected in the same location as existing varicosity models.

• (optional) Click the 3D Settings button to save your settings for later use. Learn more here.

1. Click Detect All to detect all spines on all trees using the default settings; this may take a few seconds. (See How are spines detected? below to learn about the detection process).
2. Inspect the results of the detection and, if desired, adjust the spine detection settings before detecting spines again.
3. To detect spines in addition to spines already detected, check the box, Keep existing spines.
4. Click Detect All to detect again.

This is useful for testing the spine detection settings on a small region of a large image as it restricts automatic detection to the region near the selected branch.

1. Under Detection, adjust the settings.
2. Check the box Click image to detect all on nearest branch.
3. Click near the desired branch to detect all the spines on that branch automatically.
4. Adjust detection settings and repeat as needed to identify settings for detection of spines on all branches defined in the image/stack.

1. Zoom, rotate, and pan as needed to view the spine of interest clearly.
2. Click the spine to detect it. The detected spine is displayed with a color overlay. If necessary, adjust the detection settings.
• If you see the message, "Unable to detect a spine at the selected location", you can adjust the Outer range and Detection sensitivity settings so that the software recognizes the spine. By clicking to detect a spine, you override the Minimum height and Minimum count settings.
  • Outer range: Adjust the maximum distance between the surface of the tree and the potential top of the spine head.
  • Detector sensitivity: Adjust (typically increase to >100%) to identify spines that would otherwise be considered too light or dark based on the image intensity.
3. Click the spine of interest again to detect it using the adjusted settings.

• If you can't identify parameters that produce good results for all the spines, run the automatic detection several times using different parameters each time. Check the box, Keep existing spines to retain spines that were detected in previous runs.
  • After each automatic detection, delete any detected spines that were not correctly modeled: Click the Edit button to switch to Edit mode, click a spine to select it, press the Delete key, then click the Edit button again to return to Detection mode.
  • Check the box, Keep existing spines so that the spines modeled properly with one set of parameters are not deleted or modified by the next automatic detection.
• If spines remain undetected, use the method for manually detecting individual spines described above (i.e., modify Outer range and /or Detector sensitivity then click the spine to model it).
• For a large complex cell with many processes, you can detect spines on a single branch initially to identify appropriate spine detection settings quickly: check the Click to detect all spines on nearest branch box and click the desired branch. Once you're satisfied with the detection settings, clear the checkbox and click Detect All.
• If the modeled spine head appears larger than the actual spine head, reduce detector sensitivity.
• If the base of the spine is modeled so that it appears to spread out onto the dendrite, increase the surface clearance minimum height.

Occasionally, a spine might be represented as a sphere . This happens when spines are too small for the software to create the 3D overlay.

If spines are associated with a single color-channel (that is, if a color channel was selected prior to spine detection), select that color channel to access all of the spine-editing functions. Select the color channel in either the Channel panel in the 3D environment or in the Image Adjustment tool in the 2D window.

Deleting or moving points may delete spines that are associated with these points. After editing trees, you can detect spines again to recover any deleted spines.

• Classify All: Click to assign one of four types (filopodium, mushroom, stubby, thin) to all spines based on the spine-classification settings.
• Settings: Click to view/change spine-classification settings. Spine types are determined using a combination of measured values and derived ratios that you can modify if desired by typing in the boxes or using the arrow buttons to incrementally change the values. [Also see Spine Classification Settings Panel (3D) help.]
  • Use spine total extent for classification checkbox: By default, Neurolucida 360 software uses the spine backbone length for classification. Check the box to use the spine's total extent for classification.
  • The flowchart illustrates how the program uses the settings you define to assign a type to each detected spine: 
  • The measurements are illustrated here:
  • Classify All button: Click to apply your settings and classify all identified spines in the image.
  • Reset button: Click to reset spine classification settings to their default values.
  • Return button: Click to exit and return to the spine detection window.
• Spine types are color-coded following the Automatic Classification settings; to modify the color coding, select the desired color using the color-picker pull downs for each spine type. Alternatively, you can set spine colors in the tracing preferences [File>Preferences>Tracing>scroll to Spines preferences].
• Keep existing classification: Check the box to classify additional spines without changing the current classification of spines already detected.
• To re-classify manually: Click the Edit button, click a spine to select it, and select a different type from the Type drop-down menu in the Edit Spines panel.

1. Dendritic branches are refined and displayed with an axial smear correction. Note that when you click to detect individual spines or check to the box to Click image to detect all on nearest branch, these processes are restricted to the area or branch where you click; if you have clicked detect All, the entire image/stack is included in the spine-detection process.
   
2. The program uses image segmentation to identify all the foreground voxels that are outside of the volume occupied by the dendritic model.
3. The identified voxels are clustered based on image intensity and distance to the model of the dendrite. Clustering takes into account the adjacent spines scenario and leads to the identification of spine candidates.
4. Spine candidates that meet the detection criteria listed below are rendered as a surface mesh (based on the Marching Cubes algorithm). The mesh is further processed to remove surface irregularities.

Detection criteria

A spine is detected and rendered if it meets all these criteria:

• Spine extent ≥ Minimum height

The spine extent is based on image data; it is the shortest distance from the furthest identified voxel to the surface of the dendrite. It does not necessarily follow the spine neck to the dendritic surface.

The minimum height can be defined in the Detection portion of the panel.

• Voxel count ≥ Minimum count

The voxel count is the number of voxels that comprise the detected shape of the spine. Spine candidates with fewer voxels than the minimum count are discarded.

The Minimum count can be defined in the Detection portion of the panel.

• Ratio spine extent/base ≥ 0.4

The spine extent is based on image data; it is the shortest distance from the furthest identified voxel to the surface of the dendrite.

The base is determined using the Rayburst diameters calculated for spine detection. It is an evaluation of the spread of the spine at the surface of the dendrite and it takes into account axial smear correction.

• Spine extent ≤ Outer range

The outer range is the maximum distance from the dendritic surface to the potential top of the spine head. It can be defined in the Detection portion of the panel.

Note that because rendering is based on a different algorithm than that used for spine detection and classification, the surface mesh may not reflect exactly the characteristics generated by the spine detection & classification process.

5. The spine models are displayed using multiple colors to help discriminate between adjacent spines; they do not represent spine type until you click the Classify All button or manually classify spines.

   To change the color of individual spines, and split, merge or delete spines, click the Edit button to display the Edit Spines panel.

1. Spines are classified into canonical types (stubby, mushroom, thin, or filopodium) based on your Spine Classification Settings using the method described in Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images (Rodriguez, Ehlenberger, Dickstein, Hof, & Wearne, 2008).
2. Color-coding is applied to represent spine type. The default color-coding can be changed using the color-pickers next to each spine type in the Classification section of the panel. Alternatively, you can set spine colors in the tracing preferences [File>Preferences>Tracing>scroll to Spines preferences].